During the past 30 years, meta-analysis has been an indispensable tool for revealing the hidden meaning of our research literatures. The four articles in this special section on meta-analysis illus...

Meta-Analysis: A Constantly Evolving Research Integration Tool

The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.

Conducting Meta-Analyses in R with the metafor Package

Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/
 under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

The present article presents a meta-analytic test of intergroup contact theory. With 713 independent samples from 515 studies, the meta-analysis finds that intergroup contact typically reduces intergroup prejudice. Multiple tests indicate that this finding appears not to result from either participant selection or publication biases, and the more rigorous studies yield larger mean effects. These contact effects typically generalize to the entire outgroup, and they emerge across a broad range of outgroup targets and contact settings. Similar patterns also emerge for samples with racial or ethnic targets and samples with other targets. This result suggests that contact theory, devised originally for racial and ethnic encounters, can be extended to other groups. A global indicator of Allport's optimal contact conditions demonstrates that contact under these conditions typically leads to even greater reduction in prejudice. Closer examination demonstrates that these conditions are best conceptualized as an interrelated bundle rather than as independent factors. Further, the meta-analytic findings indicate that these conditions are not essential for prejudice reduction. Hence, future work should focus on negative factors that prevent intergroup contact from diminishing prejudice as well as the development of a more comprehensive theory of intergroup contact.

A meta-analytic test of intergroup contact theory.

The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.

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Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

The Design of Diagnostic Accuracy Studies

Background Imaging tests used in our center are usually inadequate to confirm the high risk for pancreatic cancer. We aimed to use a combination of potential predictors including imaging tests to quantify the risk of pancreatic cancer and evaluate its utility. Methods This was a retrospective cohort study of patients who were suspected as having pancreatic cancer and underwent biopsy examination of pancreatic mass at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between January 1, 2013, and December 31, 2016. We retrieved data on demographics, clinical history, imaging tests, and final pancreatic diagnosis from medical records. Results Of the 206 who underwent pancreatic biopsies, the mean age was 63.6 years; 54.4% were male. Of all the biopsies, 57.8% were malignant and 42.2% were benign masses. Nine factors contributed significantly to the risk of pancreatic cancer and were noted: older age (adjusted odds ratio [aOR] =1.048; P=0.010), male gender (aOR =4.670; P=0.008), weight loss (aOR =14.810; P=0.001), abdominal pain (aOR =7.053; P=0.0.001), blood clots (aOR =20.787; P=0.014), pancreatitis (aOR =4.473; P=0.021), jaundice (aOR =7.446; P=0.003), persistent fatigue (aOR =22.015; P=0.015), and abnormal imaging tests (aOR =67.124; P=0.001). The model yielded powerful calibration (P=0.953), excellent predictive utility (area under the receiver operating characteristic curve 96.3%; 95% CI =94.1, 98.6), with optimism-corrected area under the curve bootstrap resampling of 94.9%. An optimal cut-off risk probability of 0.513 yielded a sensitivity of 94% and specificity of 84.7% for risk classification. Conclusion The study developed and validated a risk model for quantifying the risk of pancreatic cancer. Nine characteristics were associated with increased risk of pancreatic cancer. This risk assessment model is feasible and highly sensitive and could be useful to improve screening performance and the decision-making process in clinical settings in Saudi Arabia.

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Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study.

Issues in Meta‐Analysis for Diagnostic Tests

Reproducibility of ULV. Introduction: Measurement of the upper limit of vulnerability (ULV) with monophasic T wave shocks has been proposed as a patient-specific measurement of defibrillation efficacy that results in fewer episodes of ventricular fibrillation (VF) than measurement of a defibrillation efficacy curve.



Methods and Results: We sought to determine the magnitude of variance in ULV in 63 consecutive patients undergoing implantation of an implantable cardioverter defibrillator (ICD). We measured ULV as the strength at or above which VF is not induced when a stimulus is delivered at 310 msec after an 8-beat ventricular pacing drive at 400 msec. Defibrillation threshold (DFT) was measured in patients with an active can device using a biphasic waveform and the binary search method beginning at 12 J. Sixty-three patients were studied; they bad a mean age of 62 × 12 years and a mean ejection fraction of 35%± 15%. Three quarters of patients bad an ischemic cardiomyopathy. Each patient underwent 4.5 ± 0.8 measurements f ULV. Monophasic ULV correlated poorly with biphasic DFT (R between 0.19 and 0.28, P = 0.04 to 0.17). There was no change in ULV between second to third, third to fourth, and first to last measurement in 22% to 41% of patients. The reliability coefficient was 0.87. A ULV ≥ 20 J was found in eight patients. The only predictor of high ULV was a high DFT.



Conclusion: Monophasic ULVs do not closely predict biphasic active can DFTs using a standard protocol. High DFTs were predicted by high ULVs. There was little variation in the acute measurement of ULV between trials. These findings have important implications for using ULV measurements to determine changes in DFTs after interventions. The methodology of determining ULV is critical to its use for predicting DFTs and programming ICDs.

Immediate Reproducibility of Upper Limit of Vulnerability Measurements in Patients Undergoing Transvenous Implantable Cardioverter Defibrillator Implantation

This study explored risk perceptions after breast cancer risk appraisal. The study involved a randomized trial of Women's Health clinic patients (≥ 40 years old). Primary outcome was perceived breast cancer risk at baseline, 1 month, 6 months, and 18 months. Perceived breast cancer risks were higher than actual calculated risks at baseline. At baseline, 45% reported moderate/strong risk and 43% reported lower-than-average risk; 53% said that their risk was lower than 15%. Mean perceived lifetime risk was 31 out of 100. Throughout follow-up, the treatment group reported lower risks by all measures, as compared to controls. However, for African American women, perceived risk “out of 100 women” did not change. A brief health risk appraisal tends to lower breast cancer risk perceptions for at least 18 months, but the impact may vary by race/ethnicity. These findings could affect health behaviors, such as annual mammograms, which are influenced by perceived risk.

Donna K. McClish

Papers

The Design of Diagnostic Accuracy Studies

Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study.

Issues in Meta‐Analysis for Diagnostic Tests

Immediate Reproducibility of Upper Limit of Vulnerability Measurements in Patients Undergoing Transvenous Implantable Cardioverter Defibrillator Implantation

Duration of an Intervention's Impact on Perceived Breast Cancer Risk